|
|
|
|
| LEADER |
05513nam a22004213i 4500 |
| 001 |
EBC5496000 |
| 003 |
MiAaPQ |
| 005 |
20231204023214.0 |
| 006 |
m o d | |
| 007 |
cr cnu|||||||| |
| 008 |
231204s2018 xx o ||||0 eng d |
| 020 |
|
|
|a 9783662578056
|q (electronic bk.)
|
| 020 |
|
|
|z 9783662578049
|
| 035 |
|
|
|a (MiAaPQ)EBC5496000
|
| 035 |
|
|
|a (Au-PeEL)EBL5496000
|
| 035 |
|
|
|a (OCoLC)1049975285
|
| 040 |
|
|
|a MiAaPQ
|b eng
|e rda
|e pn
|c MiAaPQ
|d MiAaPQ
|
| 050 |
|
4 |
|a TH9701-9745
|
| 100 |
1 |
|
|a Niggemann, Oliver.
|
| 245 |
1 |
0 |
|a IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency :
|b Intelligent Methods for the Factory of the Future.
|
| 250 |
|
|
|a 1st ed.
|
| 264 |
|
1 |
|a Berlin, Heidelberg :
|b Springer Berlin / Heidelberg,
|c 2018.
|
| 264 |
|
4 |
|c ©2018.
|
| 300 |
|
|
|a 1 online resource (132 pages)
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 490 |
1 |
|
|a Technologien Für Die Intelligente Automation Series ;
|v v.8
|
| 505 |
0 |
|
|a Intro -- Preface -- Table of Contents -- 1 Concept and Implementation of a Software Architecture for Unifying Data Transfer in Automated Production Systems. Utilization of Industrie 4.0 Technologies for Simplifying Data Access -- 1 Introduction and Motivation -- 2 Requirements for a System Architecture to Support Industrie 4.0 Principles -- 3 State-of-the-Art of Industrie 4.0 System Architectures -- 4 Concept of a Unified Data Transfer Architecture (UDaTA) in Automated Production Systems -- 5 Evaluation -- 5.1 Expert Evaluation -- 5.2 Prototypical Lab-Scale Implementation -- 6 Conclusion and Outlook -- Acknowledgment -- References -- 2 Social Science Contributions to Engineering Projects: Looking Beyond Explicit Knowledge Through the Lenses of Social Theory -- 1 Introduction -- 2 Introducing our role(s) as social science researchers -- 2.1 What do social scientists do? -- 2.2 What did we do as IMPROVE (social science) researchers? -- 3 Empirical findings on socio-technical arrangements in HMI supported operating of smart factory plants -- 4 Social Theory Plugins -- 4.1 A systems theory of (smart) factories -- 4.2 Tacit knowledge beyond explicity -- 4.3 Conceptualizing human-machine agency -- 5 Summary and outlook -- Acknowledgments -- References -- 3 Enable learning of Hybrid Timed Automata in Absence of Discrete Events through Self-Organizing Maps -- 1 Introduction -- 2 Methodologies -- 2.1 Hybrid Timed Automata -- 2.2 Self-Organizing Map -- 2.3 Watershed Transformation -- 3 Learning hybrid timed automata without discrete events -- 4 Experiments -- 4.1 Artificial test data -- 4.2 High Rack Storage System -- 4.3 Film-Spool Unwinder -- 5 Conclusion -- Acknowledgments. -- References -- 4 Anomaly Detection and Localization for Cyber-Physical Production Systems with Self-Organizing Maps -- 1 Introduction -- 2 Self-Organizing Map.
|
| 505 |
8 |
|
|a 2.1 Anomaly detection with quantization error -- 2.2 Localization of anomalies -- 2.3 SOM trajectory tracking with timed automata -- 3 Experiments -- 3.1 Quantization error anomaly detection and anomaly localization -- 3.2 Trajectory tracking with automata -- 4 Conclusion -- Acknowledgments. -- References -- 5 A Sampling-Based Method for Robust and Efficient Fault Detection in Industrial Automation Processes -- 1 Introduction -- 2 Fault detection with stochastic process models -- 3 Fault detection for application cases with noisy measurements -- 3.1 Probability density models -- 3.2 Particle filter based fault detection -- 3.3 Parallel implementation -- 4 Evaluation and Discussion -- 4.1 Fault detection results -- 4.2 Runtime analysis -- 5 Conclusion -- Acknowledgments. -- Appendix A: Fault detection for observable process variables -- Appendix B: Metropolis Resampling -- References -- 6 Validation of similarity measures for industrial alarm flood analysis -- 1 Introduction -- 2 Clustering methodology -- 2.1 Alarm log acquisition -- 2.2 Flood detection and preprocessing -- 2.3 Alarm flood clustering -- 2.4 Distance matrix postprocessing -- 3 Evaluation methodology -- 3.1 Synthetic flood generation -- 3.2 Cluster Membership of Synthetic Floods -- 3.3 Cluster Stability -- 4 Empirical evaluation results -- 4.1 Visualization on a demonstrative set of 25 floods -- 4.2 Clustering with synthetic floods on the full dataset -- 5 Conclusion -- Acknowledgement -- References -- 7 Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause -- 1 Introduction -- 2 State of the Art of Alarm Management -- 3 Knowledge Representation -- 4 Concept for Alarm Flood Reduction -- 4.1 Learning Phase -- 4.2 Operation Phase -- 5 Conclusion -- Acknowledgment -- References.
|
| 588 |
|
|
|a Description based on publisher supplied metadata and other sources.
|
| 590 |
|
|
|a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
|
| 655 |
|
4 |
|a Electronic books.
|
| 700 |
1 |
|
|a Schüller, Peter.
|
| 776 |
0 |
8 |
|i Print version:
|a Niggemann, Oliver
|t IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency
|d Berlin, Heidelberg : Springer Berlin / Heidelberg,c2018
|z 9783662578049
|
| 797 |
2 |
|
|a ProQuest (Firm)
|
| 830 |
|
0 |
|a Technologien Für Die Intelligente Automation Series
|
| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/matrademy/detail.action?docID=5496000
|z Click to View
|
